regression

问题 As the title describes, I'm simply trying to create a shiny application that allows the user to generate linear regression plots based on an imported csv file. After importing the file the dropdown for the variables of interest should be dynamically updated. As the code below shows, I'm able to accomplish that with mtcars but I'm not able to do the same with an imported files that would have different dependent and independent variables . Thank you for your help data(mtcars) cols <- sort

问题 I need to run a SVR (supported vector regression). I have a CSV data frame.I had no problems to run the OLS regression, with one target variable and multiple regressors. But I have a problem with this part of the code. So, here is my code: import matplotlib.pyplot as plt import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR sc_X = StandardScaler() sc_y = StandardScaler() X = sc_X.fit_transform(X) y = sc_y.fit_transform(y) y_pred = sc_y.inverse

问题 I would like to loop over various regressions referencing different data subsets, however I'm unable to appropriately call different subsets. For example: dat <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10), x3 = rnorm(10) ) x.list <- list(dat$x1,dat$x2,dat$x3) dat1 <- dat[-9,] fit <- list() for(i in 1:length(x.list)){ fit[[i]] <- summary(lm(y ~ x.list[[i]], data = dat))} for(i in 1:length(x.list)){ fit[[i]] <- summary(lm(y ~ x.list[[i]], data = dat1))} Is there a way to call in

问题 I'm trying to predict a simple lagged time series regression with the dyn library in R. This question was a helpful starting point, but I'm getting some weird behaviour that I'm hoping someone can explain. Here's a minimum working example. library(dyn) # Initial data y.orig <- arima.sim(model=list(ar=c(.9)),n=10) x1.orig <- rnorm(10) data <- cbind(y=y.orig, x1=x1.orig) # This model, with a single lag term, predicts from t=2 mod1 <- dyn$lm(y ~ lag(y, -1), data) y.new <- window(y.orig, end=end

问题 I am testing to make an analysis of sentiment on a dataset. Here, I am trying to see if if there are any interesting observations between message volume and buzzs, message volume and scores... There is what my dataset looks like: > str(data) 'data.frame': 40 obs. of 11 variables: $ Date Time : POSIXct, format: "2015-07-08 09:10:00" "2015-07-08 09:10:00" ... $ Subject : chr "MMM" "ACE" "AES" "AFL" ... $ Sscore : chr "-0.2280" "-0.4415" "1.9821" "-2.9335" ... $ Smean : chr "0.2593" "0.3521" "0

问题 I am testing to make an analysis of sentiment on a dataset. Here, I am trying to see if if there are any interesting observations between message volume and buzzs, message volume and scores... There is what my dataset looks like: > str(data) 'data.frame': 40 obs. of 11 variables: $ Date Time : POSIXct, format: "2015-07-08 09:10:00" "2015-07-08 09:10:00" ... $ Subject : chr "MMM" "ACE" "AES" "AFL" ... $ Sscore : chr "-0.2280" "-0.4415" "1.9821" "-2.9335" ... $ Smean : chr "0.2593" "0.3521" "0

问题 I am testing to make an analysis of sentiment on a dataset. Here, I am trying to see if if there are any interesting observations between message volume and buzzs, message volume and scores... There is what my dataset looks like: > str(data) 'data.frame': 40 obs. of 11 variables: $ Date Time : POSIXct, format: "2015-07-08 09:10:00" "2015-07-08 09:10:00" ... $ Subject : chr "MMM" "ACE" "AES" "AFL" ... $ Sscore : chr "-0.2280" "-0.4415" "1.9821" "-2.9335" ... $ Smean : chr "0.2593" "0.3521" "0

问题 I am testing to make an analysis of sentiment on a dataset. Here, I am trying to see if if there are any interesting observations between message volume and buzzs, message volume and scores... There is what my dataset looks like: > str(data) 'data.frame': 40 obs. of 11 variables: $ Date Time : POSIXct, format: "2015-07-08 09:10:00" "2015-07-08 09:10:00" ... $ Subject : chr "MMM" "ACE" "AES" "AFL" ... $ Sscore : chr "-0.2280" "-0.4415" "1.9821" "-2.9335" ... $ Smean : chr "0.2593" "0.3521" "0

问题 How do I plot multilinear regression graph in python? import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model df = pd.read_csv("C:/Users/SAAD FAKHAR/OneDrive/Desktop/reg.csv") df This is the screen shot of csv file: reg=linear_model.LinearRegression() reg.fit(df[['area','bedroom','age']],df.price) 来源： https://stackoverflow.com/questions/64050363/how-to-plot-multilinear-regression-graph-in-python

问题 How do I plot multilinear regression graph in python? import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model df = pd.read_csv("C:/Users/SAAD FAKHAR/OneDrive/Desktop/reg.csv") df This is the screen shot of csv file: reg=linear_model.LinearRegression() reg.fit(df[['area','bedroom','age']],df.price) 来源： https://stackoverflow.com/questions/64050363/how-to-plot-multilinear-regression-graph-in-python